The development of wireless communication technology is reshaping the landscape of intelligent transportation systems, particularly in the realm of internet of vehicles (IoV). Among these, integrated sensing and communications (ISAC) has garnered widespread attention by leveraging shared hardware resources or even spectrum between sensing and communication to achieve integrated benefits. For IoV, parameters such as target position and speed estimated by ISAC can be used as prior information for resource allocation and beamforming to improve communication performance. In this paper, we focus on ISAC-enabled IoV, where radar sensing signals and communication signals are transmitted within different slots of a subframe to avoid interference. We propose a resource allocation scheme to maximize system throughput while meeting the differentiated needs of all users, where spatial division multiple access is dynamically employed based on network load. Simulation outcomes verify the efficiency of the suggested algorithm.

The derivation of ice water path (IWP) from microwave radiometer measurements is challenging. This study presents a deep learning framework for global retrieval of IWP using observations from the Microwave Humidity Sounder-II (MWHS-II) aboard the FengYun-3D (FY-3D) satellites. Two deep learning models, Deep Forest (DF21) and Quantile Regression Neural Network (QRNN) are constructed to detect ice cloud flags and retrieve IWP. By collocating MWHS-II observations with 2C-ICE, a joint product of CloudSat and CALIPSO, deep learning models learn the characteristics of IWP from MWHS-II brightness temperatures. The test results show that the MWHS-II channels provide more information on IWP than the MWHS channels, particularly the 89 GHz channel and the 118 GHz channels with an offset of ≥ 0.8 GHz. Combining the QRNN and DF21 models, the IWP retrieval results in an RMSE of 707.346 g/m2, MAPE of 65.122%, MBE of -104 g/m2, determination coefficient (R2) of 0.683, and Pearson correlation coefficient (PCC) of 0.831. Application of the models to MWHS-II observations of Tropical Cyclone CILIDA shows better agreement with 2C-ICE. All datasets exhibit a similar feature on the monthly mean scale, but the magnitudes of IWP differ. Compared to GMI-GPROF, MODIS, and ERA5 IWP products, MWHS-II results are closest to 2C-ICE. Similar results are also shown for the zonal mean data. These results show that deep learning methods efficiently and probabilistically retrieve IWP from long-term observation data of MWHS/MWHS-II.

Sea surface salinity (SSS) plays an essential role in the study of global climate change. Especially, high accuracy coastal SSS is of great significance for marine aquaculture and other aspects. SMAP is a high resolution satellite equipped with radars and radiometers for measuring soil moisture, which can also achieve observations of SSS. Based on SMAP L2B V5.0 data, to improve the coastal SSS retrieval accuracy, this paper proposes a method for retrieving SSS in coastal regions using deep neural networks (DNN) and analyzes the impact of different variables on retrieving SSS. By comparing the retrieval results of DNN with HYCOM SSS and in situ SSS, the root mean square errors (RMSE) of DNN SSS were 0.36 psu and 0.45 psu, respectively. Compared to SMAP SSS products from physical retrieval algorithm, the RMSE of DNN SSS decreased by 1.38 psu and 1.23 psu, respectively. The results indicate that using DNN to retrieve SSS in coastal regions can achieve high accuracy.

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) scatterometer in the coastal regions is evaluated. To improve coastal SSWS accuracy, we use the European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data as the reference output to establish the coastal multi-interval SSWS retrieval model based on the light gradient boosting model (LGBM). Compared with the ERA5 wind speed, the root mean square error (RMSE) of the LGBM-based model in the low, medium, and high wind speed ranges is 36.36%, 33.33%, and 34.52% lower than that of the HY-2C wind product, respectively. This paper also compares the SSWS retrieval results with the National Data Buoy Center buoy measurements. Compared to the HY-2C L2B wind product, the RMSE is reduced by 21.05%. This paper provides a dependable reference to coastal SSWS retrieval of HY-2C scatterometer observations.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology in achieving high capacity and massive connectivity. In this paper, the reliable transmission scheme of downlink NOMA systems is investigated. In particular, we divide the disc covered by the base station into several annular areas, where the receivers are randomly located following a uniform distribution. In this way, NOMA pairing is performed by randomly selecting receivers from two different areas. Firstly, we derive the closed-form expressions of bit error rate (BER) with quadrature phase-shift keying (QPSK) modulation, where the channel is modeled as small-scale Rayleigh fading and large-scale path loss. To achieve reliable communications, then, the BER performance of the receiver with the worst channel gain in each area is studied. Finally, an optimal power allocation algorithm is proposed, which obtains the minimum transmission power and optimal power allocation factor with a given BER constraint of all receivers. Extensive simulations demonstrate the accuracy of obtained BER expressions and the effectiveness of the proposed algorithm. These results provide valuable insight into realizing on reliable transmission of NOMA with randomly deployed receivers.

The weight function (WF) of the microwave radiometer channel plays an important role in the development of the theory of microwave remote sensing and the design of microwave remote sensing instruments. A deep neural network (DNN) is used to describe the relationship between the atmospheric parameters and the channel WF for Microwave Humidity and Temperature Sounder (MWHTS) onboard Fengyun-3D (FY-3D) satellite, and a DNN-based WF calculation model is developed. The experimental results show that the calculated results of WF for MWHTS from the DNN-based WF calculation model are almost equal to those of the traditional radiative transfer model, and the accuracy of the distribution of the peak WF height is high. In addition, the DNN-based WF calculation model has higher computational efficiency compared with the traditional radiative transfer model.

In the field of satellite-based microwave remote sensing, the classification of microwave observation data according to weather conditions is an important data preprocessing step in both dealing with the forward and inverse problems, which directly affects the application of observation data and the development of microwave remote sensing theory. This study designed to use the microwave radiative transfer model to classify the simulation accuracy of the observed data for the data classification preprocessing in the forward problem. And the corresponding clear-sky thresholds under clear-sky conditions are established. The experimental results show that the clear-sky data selection method for microwave remote sensing data based on the radiative transfer model has higher classification accuracy than the classification method using third-party data sources, and the operation is easy and promising for the operational meteorological application.

Microwave Humidity and Temperature Sounder (MWHTS) is one of the most important payloads on the Fengyun-3 (FY-3) satellite, and it can realize simultaneous detection of atmospheric temperature and humidity profiles. At present, the measurement data from MWHTS is not yet assimilated in the Arctic region. It is helpful to improve the initial field of the assimilation system by evaluating the accuracy of MWHTS atmospheric temperature and humidity profiles retrieval parameters in the Arctic region. In this paper, we propose a deep learning method for atmospheric temperature and humidity profiles retrieval to evaluate the accuracy of MWHTS atmospheric parameters. The results show that the RMSE of temperature profile is less than 2K, and the RMSE of humidity profile is between 10-20%. And the retrieval accuracy of LSTM is slightly better than that of DNN.

Sea surface salinity (SSS) is of great significance for studying the global water cycle and climate change. Aquarius is a satellite dedicated to measuring SSS from space. It equipped the active/passive instrument to combination measure SSS from space. This paper proposes an algorithm using machine learning of depth neural network (DNN) to retrieve SSS in coastal regions based on the Aquarius V5 Level-2 (L2) Data Product. The retrieval results are compared with HYCOM SSS and Scripps Institution of Oceanography Argo salinity (Scripps SSS). Compared with HYCOM SSS and Scripps SSS, the average root square error (RMSE) of retrieved SSS for three incident angles are 0.39psu and 0.40psu, and are 1.64psu and 1.87psu for Aquarius SSS product. The results show that compared with the physical retrieval algorithm, the DNN has better accuracy for SSS retrieval in coastal regions.

A rainfall retrieval algorithm for tropical cyclones based on dual-polarized brightness temperature (Tb) from the Fengyun 3C (FY-3C) Microwave Radiation Imager (MWRI) radiometer is presented. Since the nonlinear relationship between rain rate and Tb, the “beamfilling effect” is corrected to make the liquid water absorption closer to the Mie absorption. The rain rate retrieval is based on the unique relationship between liquid water absorption and rain rate. To evaluate the performance of MWRI retrievals, hurricane Jose is chosen as the case study, and the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) rainfall product is used as a reference dataset. The MWRI retrieved rain rates show good consistency with DPR product in hurricane Jose rainfall distribution and statistical comparison, with total bias and RMS of 0.24 mm/h and 2.36 mm/h, respectively.

To analyze the characteristic distribution of the MWRI rainfall algorithm, six record observations of typhoon Mufia are selected as the case study for this study. Six track records of MWRI observations and AMSR-E GPROF2010 rainfall products are matched and selected to make the analysis and comparisons. Averaged radial rainfall distribution from MWRI and AMSR-E rainfall products are consistent in six records. The results indicate that the MWRI retrievals are coincident with AMSR-E rain rates in temporal and spatial evaluation. MWRI radiometer can observe the entire process from formation, maturity to extinction for typhoons.

Aquarius is a satellite mission to measure sea surface salinity (SSS) from space using a combined passive/active L-band instrument. It has mapped SSS nearly 4 years since its launch in 2011. In this paper, four Machine Learning Algorithms applied without considering the physical effects of the forward model are proposed to retrieve SSS from Aquarius measurements and associated auxiliary data. The results are compared with multisource SSS data, including HYCOM SSS, Aquarius Remote Sensing Systems (RSS) SSS products, Aquarius Combined Active-Passive (CAP) SSS products, and the interpolated monthly Scripps Institution of Oceanography Argo salinity (Scripps SSS) in the South China Sea. The analysis shows that the performance of Deep Neural Network (DNN) algorithm is better than the other machine algorithms. Compared to HYCOM and Scripps SSS, the root-mean-squared error (RMSE) of the DNN algorithm is smaller than Aquarius RSS and CAP SSS products in the South China Sea.

This paper presents a rain rate retrieval algorithm for tropical cyclones (TCs) using passive microwave Fengyun-3B (FY-3B) MWRI brightness temperature (TB). Channel 18.7 and 36.5GHz of FY-3B/MWRI are used to implement retrieval algorithm. Two databases are constructed: one is 15,153 matchups of MWRI TB and NSIDC global swath ocean products for rain rate retrieving and another databset of 5,093 matchups retrieved rain rates and NSIDC rain rate products is used for error estimation of the retrievals. The retrieved rain rates show good agreement with the NSIDC surface rain rates in rain structures. Although there are slightly overestimated at heavy rain rates, the total bias is only 0.8467 mm/h and the total rms is 4.3475 mm/h even in the intensive TCs. This is submitted for the special session of “New Developments of Chinese Oceanographic and Meteorological Satellites”.

The Microwave Imager Combined Active/Passive (MICAP) is a suit of active/passive instrument package, which has been proposed for demonstrating the capability of remote sensing the sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS). In this paper, a nonlinear optimization algorithm for simultaneous retrieval of the above parameters is described. The sensitivity of active/passive microwave observations to SSS is analyzed using the nonlinear optimization algorithm. The results show that the root mean square (RMS) error on the retrieved SSS estimated by using the nonlinear optimization algorithm is enough to meet the requirement of Ocean Salinity Satellite. This is submitted for the special session of “New Developments of Chinese Oceanographic and Meteorological Satellites”.

A 1-D interferometric system at 1.4GHz, 6.9GHz, 18.7 GHz and 23.8GHz combined with a scatterometer at 1.26GHz, called microwave imager combined active/passive (MICAP), has been proposed to retrieve sea surface salinity (SSS) and to reduce geophysical errors due to surface roughness and sea surface temperature (SST). The MICAP will be a candidate payload onboard the Ocean Salinity Satellite of China. The sensitivity of active/passive microwave observations to SSS, SST and wind is analyzed and the stability requirement of the instruments is estimated, with the objective of designing an optimized satellite instrument, dedicated to an “all-weather” estimate of the SSS with high accuracy from space.